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Distributed Stochastic MPC of Linear Systems With Additive Uncertainty and Coupled Probabilistic Constraints
KTH, School of Electrical Engineering (EES), Automatic Control. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
2017 (English)In: IEEE Transactions on Automatic Control, ISSN 0018-9286, E-ISSN 1558-2523, Vol. 62, no 7, p. 3474-3481Article in journal (Refereed) Published
Abstract [en]

This technical note develops a new form of distributed stochastic model predictive control (DSMPC) algorithm for a group of linear stochastic subsystems subject to additive uncertainty and coupled probabilistic constraints. We provide an appropriate way to design the DSMPC algorithm by extending a centralized SMPC (CSMPC) scheme. To achieve the satisfaction of coupled probabilistic constraints in a distributed manner, only one subsystem is permitted to optimize at each time step. In addition, by making explicit use of the probabilistic distribution of the uncertainties, probabilistic constraints are converted into a set of deterministic constraints for the predictions of nominal models. The distributed controller can achieve recursive feasibility and ensure closed-loop stability for any choice of update sequence. Numerical examples illustrate the efficacy of the algorithm.

Place, publisher, year, edition, pages
IEEE, 2017. Vol. 62, no 7, p. 3474-3481
Keyword [en]
Distributed control, model predictive control (MPC), probabilistic constraints, stochastic systems
National Category
Control Engineering
Identifiers
URN: urn:nbn:se:kth:diva-210998DOI: 10.1109/TAC.2016.2612822ISI: 000404299300033OAI: oai:DiVA.org:kth-210998DiVA, id: diva2:1129744
Note

QC 20170807

Available from: 2017-08-07 Created: 2017-08-07 Last updated: 2017-08-07Bibliographically approved

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Gao, Yulong

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